Digital innovation in the pharmaceuticals and chemicals industries

The pharmaceutical and chemicals industries are no strangers to digital technology, with decades of experimentation using data and statistical techniques to improve productivity and innovation. But the results were historically disappointing relative to the promise.

Over the past two or three years, the pace of digital transformation is increasing thanks to the improved performance, power, and adaptability of tools, and investments in cloud computing, data architecture, and visualization technologies. There are also an increasing number of use cases for machine learning and, in future, quantum computing, which will accelerate the development of molecules and formulations. 

The broad digital transformation taking place in R&D is allowing researchers to automate time-consuming manual processes and opening new research horizons in thorny problems that have failed to elicit breakthroughs. This new report, based on interviews with R&D executives at companies including Novartis, Roche, Merck, Syngenta, and BASF, explores the use cases, best practices, and roadmaps for digitalizing science.

Exploring patterns in complex datasets

Rich, accessible, and shareable data are the fuel on which today’s breakthrough analytics and computing tools rely. To ensure that datasets are usable for scientific purposes, leading companies are focusing on FAIR data principles (findable, accessible, interoperable, and reusable), developing robust metadata and governance protocols, and using advanced analytics and data visualization tools.

Digital transformation is opening up R&D horizons in areas such as genomics that could lead to breakthroughs in precision medicine. It is also creating opportunities for decentralized clinical trials, unleashing future innovations in digi-ceuticals and healthcare wearables.

Reaching the right study faster

Experiments and clinical trials carry a huge cost for both industries, both financially and in terms of human and scientific resources. Advanced simulation, modelling, AI-based analytics, and quantum computing are helping identify the strongest candidate for new therapies, materials, or products, allowing only the most promising to proceed to the costly experimental phase. 

Organizational overhaul

R&D leaders foster bottom-up innovation by giving research teams freedom to experiment with new technologies and techniques. They also drive top-down strategic initiatives for sharing ideas, harmonizing systems, and channeling digital transformation budgets. As in any industry, AI and automation are changing ways of working in scientific research. Rather than being seen as a threat to research careers, leading organizations in pharma and chemicals are demonstrating that digital provides new opportunities for collaboration and the breaking down of silos. They celebrate wins, encourage feedback, and nurture open discussions about culture shifts in the workplace. 

Download the full report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

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Big pharma is using AI and machine learning in drug discovery and development to save lives

Summary List PlacementThe pharmaceutical industry has been slow-moving when it comes to adopting digital health technology, and pharma companies overall have taken a long time to implement AI and machine learning strategies — making broad-scale digital transformation difficult.

There is ample opportunity for drug discovery and development, but it relies on the ability of companies to implement advanced health tech into everyday strategies. 
While the healthcare industry is rapidly adopting digital tech, the pharma industry is lagging on digital maturity, and any measures even early movers are taking to catch up are patchworked due to a lack of strategy and digital-focused leadership.
AI & Machine Learning in the Drug Development Process
An incredible amount of time and money goes into drug development — bringing a drug to market costs about $2.8 billion dollars over 12+ years, according to Taconic Biosciences’ tally.  
Utilizing AI and machine learning can help at every stage of the drug discovery process. Healthcare AI startups were able to raise over  $2 billion in Q3 2020, and those using AI to streamline the drug making process were the recipients of some of the heftiest sums compared with startups deploying the tech in other healthcare segments.
AI in Drug Discovery (Phase I)
The drug discovery process ranges from reading and analyzing already existing literature, to testing the ways potential drugs interact with targets. According to Insider Intelligence’ AI in Drug Discovery and Development report, AI could curb drug discovery costs for companies by as much as 70%.
AI in Preclinical Development (Phase 2)
The preclinical development phase of drug discovery involves testing potential drug targets on animal models. Utilizing AI during this phase could help trials run smoothly and enable researchers to more quickly and successfully predict how a drug might interact with the animal model.
AI in Clinical Trials (Phase 3)
After making it through the preclinical development phase, and receiving approval from the FDA, researchers begin testing the drug with human participants. Overall, this is a four-phase process and usually considered the longest and most expensive stage in the drug making journey. 
AI can facilitate participant monitoring during clinical trials—generating a larger set of data more quickly—and aid in participant retention by personalizing the trial experience. 
Pharma Investments in AI
Big tech investments in pharma are at an all time high. Specifically, big tech firms with a broad range of AI and cloud solutions make valuable partners to drugmakers, which have varied needs when it comes to AI.

For example, Moderna leverages Amazon’s AWS cloud platform to speed up its drug development process. And while Moderna has recently made headlines as a top contestant in the race to develop a coronavirus vaccine, the company should also be recognized for its success in developing a cancer vaccine in just 40 days while leaning on AWS. 
Moderna is just one example of the many pharma companies taking advantage of Big Tech’s growing interest in the digital health industry. And Insider Intelligence expects Big Tech to continue using their AI brawn to forge pharma tie-ups.
Here are the companies analyzed in the report:

AbbVie
Amazon
Apple
AstraZeneca
Atomwise
Biofourmis
Eli Lilly
Exscientia
Google
Insilico
Litmus Health
Microsoft
Moderna
Novartis
Otsuka
Pfizer
Recursion Pharmaceuticals
Repurpose.AI
Roche
Sanofi
TriNetX
Verily
Verisim
XtalPi

Interested in getting the full report? Here’s how you can gain access:

Join other Insider Intelligence clients who receive this report, along with thousands of other Digital Health forecasts, briefings, charts, and research reports to their inboxes. > > Become a Client
Purchase the individual report from our store. > > Buy The Report Here
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